Applications of Quantum Computing Beyond IT

“We make cars. What could quantum possibly do for us?” a representative from a major car company asked me this week. “And besides,” they added, “we already use AI — so we’re probably covered.” Fair question. And no, quantum won’t make trucks teleport (ever). But it will reshape how cars are designed, produced, powered, and maintained — often together with #AI. In fact, companies like Volkswagen Group, Mercedes-Benz AG, and Porsche AG are already exploring quantum use cases today: ⚡ Battery breakthroughs - car manufacturers are working with companies developing quantum hardware to simulate lithium-sulfur battery materials using #QuantumComputing. The idea is to improve charge capacity, energy density, and battery life for electric vehicles. ⚡ ⚡ Production optimization - another use case is to apply quantum to simulate welding and other processes, identifying potential defects before they happen on the factory floor. And this is just the beginning. Let’s unpack how quantum will act as a force multiplier for AI — especially in industrial sectors like automotive, logistics, and mobility: 🔹 Faster training of AI models Training large models for autonomous driving or fleet management takes serious compute. Quantum computing could speed up complex math operations in deep learning — shaving training time from months to days. 🔹 Smarter supply chain optimization Quantum algorithms like QAOA could help AI find faster, better solutions to complex problems like routing, scheduling, and resource allocation — critical in global automotive supply chains. 🔹 Next-gen R&D simulations AI + quantum chemistry = a leap in simulating materials, structures, and battery components, before building anything physical. That means faster, smarter innovation. 🔹 Safer autonomy through better NLP Vehicle perception systems rely on understanding nuance and context. Quantum-enhanced NLP may help AI interpret rare edge cases more accurately — a big win for autonomous driving safety. 🔹 Richer data analytics Quantum machine learning could unlock insights from massive, high-dimensional datasets — from predictive maintenance to customer behavior modeling. Bottom line? Quantum won’t replace AI. But it will unlock a new scale of possibility. We’re moving from “maybe someday” to “what can we pilot now?” And those who start early — even with hybrid quantum-classical approaches — will build real strategic advantage. Curious what you think: 👉 Where do you see quantum enhancing AI in your industry? Let’s exchange ideas, in comments below!

Can we find hidden tunnels using quantum computers? For our quantum computing final project, my team and I decided to find out. Detecting subsurface structures, such as tunnels, aquifers, or voids, is impossible using classical methods, as classical gravimeters are plagued by vibrations, tilt, and drift. That's where Akshat, Aakrisht, Sahana, Landon, and I's Physics 19N final project, GraviQ: Simulating Subsurface Mapping with a Qubit-Based Gravimeter, comes in. By simulating an "hourglass" configuration of two atom clouds, we can measure the vertical gravity gradient (Gzzs) while canceling out the environmental noise. We built our procedure in three steps: 1) We generated 2D density grids representing rock, ore, tunnels, and caves to create synthetic environments. 2) We used Qiskit, a quantum simulator to model a Ramsey interferometer. We mapped subsurface density to qubit phase shifts, simulating the behavior of a real quantum sensor (including decoherence and sampling noise). 3) We fed the resulting Gzz maps into a U-Net machine learning segmentation model. The tentative results are notable. Despite the simulated noise, our model achieved ~95% accuracy in detecting tunnel presence and a Dice score of up to 0.85 for localization. We believe if we can replicate this in real life, the applications are far-reaching in fields ranging from civil engineering and infrastructure, to mineral extraction, to even space exploration. Here are links to our code and slides: GitHub: https://lnkd.in/eRUYWvj6 Slides: https://lnkd.in/eeBv-F5h Huge thanks to my teammates Akshat Kannan, Aakrisht Mehra, Sahana, and Landon Moceri, and Professor Hari Manoharan for the guidance and discussions along the way. Happy to chat with anyone interested in or working on quantum sensing or related research!

One-Atom Quantum Computer Simulates Molecular Reactions with Unprecedented Efficiency Introduction: A Quantum Breakthrough in Chemistry Simulation A research team has successfully used a one-atom quantum computer to simulate how real molecules evolve over time after absorbing light—something that has long challenged classical computing. Published in the Journal of the American Chemical Society, this study represents a milestone in quantum chemistry and demonstrates a method that’s reportedly a million times more efficient than conventional quantum simulation techniques. Key Innovations and Findings: 1. Simulating Molecular Change, Not Just Static Properties • Traditional quantum computers have so far only been used to calculate static molecular properties—like energy levels or bond strengths. • This new method allows for dynamic simulations: modeling how molecules respond to light, including electron excitation, atomic vibration, and bond reshuffling—processes critical to photosynthesis, solar cells, and photomedicine. 2. Trapped Ion Technology • The researchers used a trapped calcium ion, essentially a one-atom quantum processor, as their simulation platform. • By manipulating the ion’s quantum state, they recreated the time-evolution of molecular systems at femtosecond (quadrillionth of a second) resolution—matching the timescales of real photochemical reactions. 3. Radical Leap in Efficiency • The study claims a million-fold increase in resource efficiency compared to standard quantum simulation techniques. • This was achieved through a novel algorithmic approach that minimizes the quantum operations needed to model time-dependent processes. 4. Real-World Applications Simulated • The team successfully modeled specific molecular transformations triggered by light, a foundational step for future advances in: • Drug development • Solar energy design • Photodynamic cancer therapies • DNA damage mitigation research Why This Matters: A New Quantum Era in Chemistry • Understanding photochemical dynamics is central to both biological function and energy technologies, yet has been computationally intractable—until now. • This study shows that even ultra-small quantum systems can tackle complex, real-world problems, provided the algorithms are smart enough. • It suggests a future where chemical simulation becomes routine on small, highly optimized quantum devices, long before fault-tolerant universal quantum computers arrive. Conclusion: One Atom, Big Impact By simulating the fleeting, intricate dance of molecules under light, a single-ion quantum computer has demonstrated that quantum chemistry’s future may be smaller, faster, and more accessible than expected. This research not only overcomes a major bottleneck in simulation but also signals a powerful new direction for time-resolved quantum modeling. Keith King https://lnkd.in/gHPvUttw

Imagine a technology that could radically transform how we compute, solve complex problems, and address global challenges. This is the promise of quantum computing. A striking example of its potential is transforming the fertilizer production industry, which significantly impacts global electricity consumption and greenhouse gas emissions, accounting for about 1% of the world's electricity use. Quantum computing, based on quantum mechanics principles, introduces systems capable of existing in multiple states simultaneously, dramatically speeding up complex computations. This revolutionary technology can redefine AI, cybersecurity, and research and development while tackling critical global issues like climate change. The emergence of quantum computing necessitates new programming languages, development tools, and data processing techniques. Quantum computing is crucial in designing energy storages for renewable energy systems supporting initiatives like the International Solar Alliance. By improving the efficiency of these systems, quantum computing aligns with global clean energy goals, aiding in the transition to sustainable energy sources. The impact of quantum computing on AI is profound. It promises new, interdisciplinary innovations, redefining problem-solving and technological development. Its ability to simulate complex systems, from molecular structures to environmental systems, is fascinating, enabling AI to predict the behaviour of molecules to the dynamics of ecosystems. In security, quantum computing presents both challenges and opportunities. It could render current cryptography systems obsolete, prompting concerns in digital security. Simultaneously, it's spurring the development of quantum-resistant algorithms, a key focus for entities prioritizing security, including national governments. In R&D, particularly in simulating complex physical and chemical processes quantum can be a game changer. This can significantly reduce the time and costs associated with innovation, leading to rapid advancements in pharmaceuticals, materials engineering, and environmental science. We must prioritize education and training in quantum computing principles and applications as we navigate this quantum leap. This is essential to ensure equitable access to quantum technology and avoid deepening global inequalities or Quantum colonization. As governments worldwide recognize the transformative potential of quantum technologies, they are formulating policies to guide their ethical development and use. These initiatives, aiming to foster research, promote industry collaboration, and build necessary quantum infrastructure, ensure that quantum advancements are secure, responsible, and beneficial for society. #BigIdeas2024 Note: I generated the Image using DALL-E

The Schrödinger Equation Gets Practical: Quantum Algorithm Speeds Up Real-World Simulations Quantum computing has taken a major leap forward with a new algorithm designed to simulate coupled harmonic oscillators, systems that model everything from molecular vibrations to bridges and neural networks. By reformulating the dynamics of these oscillators into the Schrödinger equation and applying Hamiltonian simulation methods, researchers have shown that complex physical systems can be simulated exponentially faster on a quantum computer than with traditional algorithms. This breakthrough demonstrates not only a practical use of the Schrödinger equation but also the deep connection between quantum dynamics and classical mechanics. The study introduces two powerful quantum algorithms that reduce the required resources to only about log(N) qubits for N oscillators, compared to the massive computational demands of classical methods. This exponential speedup could transform fields such as engineering, chemistry, neuroscience, and material science, where coupled oscillators serve as the backbone of real-world modeling. By bridging theory and application, this research underscores how quantum computing is redefining problem-solving in physics and beyond. With proven exponential advantages and the ability to simulate systems once thought computationally impossible, this quantum algorithm marks a milestone in quantum simulation, Hamiltonian dynamics, and real-world physics applications. The findings point toward a future where quantum computers can accelerate scientific discovery, optimize engineering designs, and even open new frontiers in AI and computational neuroscience. #QuantumComputing #SchrodingerEquation #HamiltonianSimulation #QuantumAlgorithm #CoupledOscillators #QuantumPhysics #ComputationalScience #Neuroscience #Chemistry #Engineering

As reported by World Economic Forum, #quantumcomputing is emerging as a transformative solution for #energy forecasting and optimization, addressing the growing complexities of renewable energy integration and evolving consumption patterns. Traditional computing struggles to manage the variability of #solar and #wind energy, coupled with the unpredictability of rising electrification from #electricvehicles and smart appliances. These challenges require advanced computational capabilities to balance supply and demand effectively. Quantum computing leverages qubits, which process vast datasets simultaneously, enabling highly accurate energy forecasting. By incorporating weather patterns, historical usage data, and grid conditions, quantum algorithms enhance predictions, allowing energy providers to better anticipate fluctuations in renewable generation and align energy distribution with demand. This reduces inefficiencies, minimizes energy waste, and ensures a stable power supply. Beyond forecasting, quantum computing optimizes power grid operations by identifying potential bottlenecks, improving load balancing, and enabling real-time grid management. This results in a more resilient and adaptive energy infrastructure. Additionally, quantum computing enhances energy storage efficiency and demand-response strategies by determining the best times to charge and discharge energy, ensuring alignment with grid conditions. Practical applications are already demonstrating the benefits of quantum computing, from optimizing renewable integration to improving electric vehicle charging schedules. As the #technology advances, it will play an increasingly critical role in shaping the future of energy management. By offering real-time optimization, increased efficiency, and more sustainable energy solutions, quantum computing is set to revolutionize the #global #energy sector, ensuring a cleaner, more resilient, and reliable energy #ecosystem.

What happens when Southeast Asia finally gets access to quantum computing? By 2026, the National Quantum Office (NQO) and Quantinuum will install the region’s first commercial quantum computer in Singapore. The Helios system will be part of a new R&D and Operations Centre focusing on developing practical applications across logistics, finance, and biotechnology (and dare I hope ...biohacking??) Quantum computing uses qubits instead of classical bits, enabling calculations across multiple states at once. This means complex modelling and optimisation problems are solved far faster than traditional systems ever could. In life sciences, that means the ability to simulate molecules and proteins at a level of precision that could speed up drug discovery and unlock new approaches to personalised medicine — even biohacking. The challenges that remain are hardware maturity, high access costs, and a limited pool of regional expertise. But, the foundation is being laid, and for Southeast Asia, it’s a significant leap in capability. This isn't about getting it running; it’ll be what we choose to do once we can.

The high-energy physics (HEP) community is particularly poised to benefit from quantum computing due to the intrinsic quantum nature of its most complex computational challenges. These include theoretical models that are hard to tackle with classical computers and the complex data analysis required for the interpretation of experiments like those carried out at the Large Hadron Collider. In a collaborative effort led by CERN, DESY, and IBM, a roadmap has been created to outline the current state of quantum computing in the HEP community. This roadmap highlights both theoretical and experimental applications that can be pursued with near-term quantum computers. This work emphasizes the potential of quantum computing to address challenging problems in HEP and aims to encourage continued exploration and development of quantum applications in this field. I look forward to see the roadmap overviewed in this paper get closer to fruition, and to the next published paper that will come of our working groups, pushing for near-term use cases for quantum computing. Read the paper here https://lnkd.in/eCibpTg2

Our R&D team at Stellium Inc. has recently been diving deep into concepts like quantum machine learning and quantum PCA, with the goal of identifying the best levers out there to address supply chain challenges with emerging tech. After our most recent midmonth Innov8 workshop, I’m no longer surprised by the fact that the market size for quantum computing is projected to grow at a CAGR of 18+% during the forecast period 2025-2032. The modern supply chain, as we all know, forms a sophisticated network of interconnected elements, where decision-making amid complexity often involves significant uncertainty. Effective management hinges on processing vast streams of real-time data to minimize costs and fulfill customer demands. As these global systems expand, classical computing approaches are reaching their limits in processing speed and handling intricate modeling. Enter Quantum Computing: 🎱 Quantum solutions are exceptionally positioned to tackle the most demanding challenges in logistics, including route optimization, operational efficiency, and emissions reduction. This capability stems from foundational quantum mechanics principles such as Superposition, Interference and Entanglement, that are redefining computational processes. For supply chain executives, this really boils down to resolving complex problems more rapidly than classical algorithms, including those on supercomputers. The aim is to develop responsive analytics through dramatically reduced computation times. Large scale supply chain optimization problems are no longer going to need hrs or days but rather seconds. Industry researchers and a few enterprises are already applying techniques such as the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing. These methods reformulate combinatorial challenges, like the traveling salesman problem in transportation logistics into quantum frameworks, identifying optimal solutions by reaching the ‘minimum energy state’. We are now seeing progress beyond conceptual stages to practical Proofs of Concept (PoCs): • BMW Group applied recursive QAOA to address partitioning issues in supply chain resource allocation. • Volkswagen demonstrated real-time optimal routing through urban traffic variations. • Coca-Cola Bottlers Japan Inc. utilized quantum computing to refine their logistics for a network exceeding 700,000 vending machines. Quantum-powered logistics and supply chain innovations are poised for substantial growth in the years ahead. Forward-thinking organizations recognize the impending transformation and are proactively preparing to become quantum-ready. At Stellium Inc., we are in our early R&D stage when it comes to exploring quantum use cases and strategic partnerships. I am bullish about the impact it’s going to have on supply chain and recognize the need to invest in it right now. DM if you’re interested to discuss more over coffee at Dubai this coming week or at SAP Connect early October in Vegas.